Experimental Analysis of Pipelining Community Detection and Recommender Systems

Ryan Dutra de Abreu, Laura Silva de Assis, Douglas Cardoso

2023

Abstract

Community detection and recommender systems are two subjects of the highest relevance among data-oriented computational methods, considering their current applications in various contexts. This work investigated how pipelining these tasks may lead to better recommendations than those obtained without awareness of implicit communities. We experimentally assessed various combinations of methods for community detection and recommendation algorithms, as well as synthetic and real datasets. This targeted to unveil interesting patterns in the behavior of the resulting systems. Our results show that insights into communities can significantly improve both the effectiveness and efficiency of recommendation algorithms in some favorable scenarios. These findings can be used to help data science researchers and practitioners to better understand the benefits and limitations of this methodology.

Paper Citation

in Harvard Style

Dutra de Abreu R., Silva de Assis L. and Cardoso D. (2023). Experimental Analysis of Pipelining Community Detection and Recommender Systems. In Proceedings of the 19th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST; ISBN 978-989-758-672-9, SciTePress, pages 496-503. DOI: 10.5220/0012237000003584

in Bibtex Style

@conference{webist23,
author={Ryan Dutra de Abreu and Laura Silva de Assis and Douglas Cardoso},
title={Experimental Analysis of Pipelining Community Detection and Recommender Systems},
booktitle={Proceedings of the 19th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST},
year={2023},
pages={496-503},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012237000003584},
isbn={978-989-758-672-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST
TI - Experimental Analysis of Pipelining Community Detection and Recommender Systems
SN - 978-989-758-672-9
AU - Dutra de Abreu R.
AU - Silva de Assis L.
AU - Cardoso D.
PY - 2023
SP - 496
EP - 503
DO - 10.5220/0012237000003584
PB - SciTePress